Remote sensing imagery is used in diverse fields such as urban planning, military, intelligence and disaster monitoring. Many of the applications require the ability to detect changes between images taken at successive time points. In spite of the fact that known methods exist in the art for detecting differences or changes between images taken from the same or similar angle, these methods are error prone due to the high rate of false alarm (FA) change detections. False alarm detection is typically caused by two dimensional differences (such as shadow changes) between the images that do not actually reflect a true, physical, three dimensional (“volumetric”) difference such as a structure appearing in a location previously unoccupied, or a vehicle no longer appearing in a location where it appeared in a previous image. Shadow change, for example, may be caused by a change in the illuminating source position. In addition, other factors that change with time such as seasonal changes, weather conditions, level of moisture in the ground and other factors would create changes in the way objects are reflected in a scene at different times. However, volumetric, three dimensional changes would likely reflect the appearance, disappearance or displacement of objects with true physical volume such as houses, automobiles, trash bins and so on. In order to decrease FA errors it is beneficial to detect and classify those true volumetric changes.
A preferred approach used to increase the detection sensitivity of volumetric difference would be to first create three dimensional (“3D”) representations of the area (each corresponding to a different time point), and then compare those 3D representations in order to detect 3D changes. In order to create a 3D representation of an area, two or more images of the same scanned area each taken from different angles (“stereoscopic images”) are used. However, despite the fact that the above approach would theoretically improve the volumetric detection sensitivity, it is hampered by the technological complexity of registering images of the same scanned area taken from different angles (“stereo-matching”). Stereo images taken from increased angle difference (termed “wide baseline stereoscopic images”, also known in the art as stereoscopic images having high base over height (“B over H”) ratio), are more difficult to stereo-match.
In the text that follows, the term “stereo-matching” represents the process of associating points between two stereoscopic images (one point from each image) in order to generate pairs of “correspondence points”. Each pair of points is assumed to represent the same physical entity in the scene as reflected in the two stereoscopic images. The product of the stereo-matching process is either a sparse set of correspondence points (limited number of pairs) or a dense set of correspondence points (also known in the art as “dense stereo matching”) in which a pair of correspondence points is defined for every pixel covered by both images of the stereo-pair. A depth map (a three dimension representation of the scenery) can be produced from a dense set of correspondence points.
The following is a list of related art:
US2006/0239537 published Oct. 26, 2006 and entitled “Automatic processing of aerial images” discloses a change detection apparatus for detection of changes between first and second stereoscopic image pairs obtained at different times of a substantially similar view. The apparatus comprises a two-dimensional image filter for comparing first and second image pairs to obtain an initial list of change candidates from two-dimensional information in the image pairs, and a three-dimensional image filter for comparing the image pairs at locations of the change candidates using three-dimensional image information. The apparatus retains those change candidates correlating with three-dimensional image change and rejects change candidates not correlating with three-dimensional image change, and produces a refined list of change candidates.
U.S. Pat. No. 4,975,704 entitled “Method for detecting surface motions and mapping small terrestrial or planetary surface deformations with synthetic aperture radar” discloses a technique based on synthetic aperture radar (SAR) interferrometry is used to measure small surface deformations (1 cm or less) with good resolution (10 in) over large areas (50 km). Two SAR images are made of a scene by two spaced antennas and a difference interferrogram of the scene is made. After unwrapping phases of pixels of the difference interferrogram, surface motion or deformation changes of the surface are observed. A second interferrogram of the same scene is made from a different pair of images, at least one of which is made after some elapsed time. The second interferrogram is then compared with the first interferrogram to detect changes in line of sight position of pixels. By resolving line of sight observations into their vector components in other sets of interferrograms along at least one other direction, lateral motions may be recovered in their entirety. There is a need in the art for image stereo-matching of images taken at widely different viewing angles e.g. wide baseline stereo images.
There is a further need in the art to compare images taken at different times and to reliably detect volumetric changes between them such as a new construction site or military facility.